Application of distributional cost-effectiveness analysis methodology in real-world studies: a scoping review protocol
Bibliographic record
Abstract
INTRODUCTION: Healthcare systems face the challenge of managing limited resources while addressing the growing demand for care and the need for equitable access. Traditional cost-effectiveness analyses focus on maximising health benefits but often fail to account for how these benefits are distributed across various populations, potentially increasing health inequities. As a result, there is increasing interest in distributional cost-effectiveness analysis (DCEA), which incorporates equity considerations by explicitly assessing how health outcomes and costs are shared among diverse populations. This scoping review explores the practical application of DCEA methodology in evaluating programs and interventions. We seek to learn more about the barriers to DCEA's application, highlighting its practical challenges, limited use globally and the steps necessary to integrate equity more effectively into implementing and adopting programs and interventions into healthcare policy and resource allocation. METHODS AND ANALYSIS: To evaluate the use of DCEA in the literature, a scoping review will follow Preferred Reporting Items for Systematic Reviews and Meta-Analyses-Scoping Review Extension guidelines. Systematic searches will be performed across scientific databases (MEDLINE, SCOPUS, BASE, APA Psych and JSTOR), grey literature sources (Google Custom Search Engine), and handsearching to identify eligible articles published from January 2015 to March 2025. No limits will be placed on language. Reviewers will independently chart data from eligible studies using standardised data abstraction. The collected information will be synthesised both quantitatively and narratively. ETHICS AND DISSEMINATION: Formal ethical approval is not necessary as this study will not collect primary data. The findings will be shared with professional networks, published in conference proceedings and submitted for peer-reviewed publication.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.249 | 0.254 |
| Meta-epidemiology (narrow) | 0.006 | 0.007 |
| Meta-epidemiology (broad) | 0.013 | 0.015 |
| Bibliometrics | 0.022 | 0.020 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.013 | 0.010 |
| Insufficient payload (model declined to judge) | 0.071 | 0.022 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".